Recent research has demonstrated that humans are able to implicitly encode and retain repeating patterns in meaningless auditory noise. Our study aimed at testing the robustness of long-term implicit recognition memory for these learned patterns. Participants performed a cyclic/non-cyclic discrimination task, during which they were presented with either 1-s cyclic noises (CNs) (the two halves of the noise were identical) or 1-s plain random noises (Ns). Among CNs and Ns presented once, target CNs were implicitly presented multiple times within a block, and implicit recognition of these target CNs was tested 4 weeks later using a similar cyclic/non-cyclic discrimination task. Furthermore, robustness of implicit recognition memory was tested by presenting participants with looped (shifting the origin) and scrambled (chopping sounds into 10&minus; and 20-ms bits before shuffling) versions of the target CNs. We found that participants had robust implicit recognition memory for learned noise patterns after 4 weeks, right from the first presentation. Additionally, this memory was remarkably resistant to acoustic transformations, such as looping and scrambling of the sounds. Finally, implicit recognition of sounds was dependent on participant's discrimination performance during learning. Our findings suggest that meaningless temporal features as short as 10 ms can be implicitly stored in long-term auditory memory. Moreover, successful encoding and storage of such fine features may vary between participants, possibly depending on individual attention and auditory discrimination abilities.

Meaningless auditory patterns could be implicitly encoded and stored in long-term memory.

Acoustic transformations of learned meaningless patterns could be implicitly recognized after 4 weeks.

Implicit long-term memories can be formed for meaningless auditory features as short as 10 ms.

Figure 1: Exemplars of 1-s Gaussian white noises (sampling frequency = 44.1 kHz) and acoustic transformations used in the experiment. (A) Cyclic noise (CN) vs. non-cyclic noise (N): Gaussian noises typically show small amplitude variations over time. The first and second halves of a CN are identical, while an N is completely random. (B) Transformations used to loop and scramble the learned CNs in the testing session. For looping, a random time point was chosen in the first half of the sound and the sound portion preceding this time point was shifted to the end. For scrambling, the first half of the cyclic sound was cut into segments of 20 ms for version 1 and 10 ms for version 2, the segments were randomly shuffled and the resulting 500-ms sound was played back to back to create a scrambled CN. (C) Looped and Scrambled sounds: amplitude variations over time of exemplar looped and scrambled (20 ms) versions of the CN shown in (A). The color scheme of (A,C) is graded as a function of sound amplitudes, in order to facilitate identification of repeating features.

Mentions:
Stimuli were programmed and generated using MATLAB R2013 (http://www.mathworks.com/). The sound stimuli were sequences of normally-distributed, 16-bit pseudo-random numbers with a zero mean, which were played at a sampling frequency of 44.1 KHz. To ensure that the sounds are different every time, we reset the seed of the pseudorandom number generator of MATLAB on every trial. We constructed Cyclic (CN) and Non-Cyclic (N) stimuli, both lasting 1 s in duration (Audio samples can be found at http://m4.ups-tlse.fr/; See Supplementary Material for Audios 1 and 2 which are exemplar CN and N, respectively). A CN was generated as a 500-ms pseudo-random segment of sound that was presented twice back to back (cycled). An N was generated as a 1000-ms pseudo-random segment. The spectrograms of such Gaussian white noises are flat, with no distinctive variations in frequency over time. Therefore, to illustrate the cyclic nature of these sounds, we plotted the actual amplitude variations over time (Figure 1A). This shows that the amplitude variations in the first and second halves are identical in a CN but not in an N. Over the experiment, participants were presented with 4 variations of the CNs (explained below) while all the Ns were uniquely generated and heard only once. The generation and exemplar amplitude variations in modified CNs are illustrated in Figures 1B,C.

Figure 1: Exemplars of 1-s Gaussian white noises (sampling frequency = 44.1 kHz) and acoustic transformations used in the experiment. (A) Cyclic noise (CN) vs. non-cyclic noise (N): Gaussian noises typically show small amplitude variations over time. The first and second halves of a CN are identical, while an N is completely random. (B) Transformations used to loop and scramble the learned CNs in the testing session. For looping, a random time point was chosen in the first half of the sound and the sound portion preceding this time point was shifted to the end. For scrambling, the first half of the cyclic sound was cut into segments of 20 ms for version 1 and 10 ms for version 2, the segments were randomly shuffled and the resulting 500-ms sound was played back to back to create a scrambled CN. (C) Looped and Scrambled sounds: amplitude variations over time of exemplar looped and scrambled (20 ms) versions of the CN shown in (A). The color scheme of (A,C) is graded as a function of sound amplitudes, in order to facilitate identification of repeating features.

Mentions:
Stimuli were programmed and generated using MATLAB R2013 (http://www.mathworks.com/). The sound stimuli were sequences of normally-distributed, 16-bit pseudo-random numbers with a zero mean, which were played at a sampling frequency of 44.1 KHz. To ensure that the sounds are different every time, we reset the seed of the pseudorandom number generator of MATLAB on every trial. We constructed Cyclic (CN) and Non-Cyclic (N) stimuli, both lasting 1 s in duration (Audio samples can be found at http://m4.ups-tlse.fr/; See Supplementary Material for Audios 1 and 2 which are exemplar CN and N, respectively). A CN was generated as a 500-ms pseudo-random segment of sound that was presented twice back to back (cycled). An N was generated as a 1000-ms pseudo-random segment. The spectrograms of such Gaussian white noises are flat, with no distinctive variations in frequency over time. Therefore, to illustrate the cyclic nature of these sounds, we plotted the actual amplitude variations over time (Figure 1A). This shows that the amplitude variations in the first and second halves are identical in a CN but not in an N. Over the experiment, participants were presented with 4 variations of the CNs (explained below) while all the Ns were uniquely generated and heard only once. The generation and exemplar amplitude variations in modified CNs are illustrated in Figures 1B,C.

Recent research has demonstrated that humans are able to implicitly encode and retain repeating patterns in meaningless auditory noise. Our study aimed at testing the robustness of long-term implicit recognition memory for these learned patterns. Participants performed a cyclic/non-cyclic discrimination task, during which they were presented with either 1-s cyclic noises (CNs) (the two halves of the noise were identical) or 1-s plain random noises (Ns). Among CNs and Ns presented once, target CNs were implicitly presented multiple times within a block, and implicit recognition of these target CNs was tested 4 weeks later using a similar cyclic/non-cyclic discrimination task. Furthermore, robustness of implicit recognition memory was tested by presenting participants with looped (shifting the origin) and scrambled (chopping sounds into 10&minus; and 20-ms bits before shuffling) versions of the target CNs. We found that participants had robust implicit recognition memory for learned noise patterns after 4 weeks, right from the first presentation. Additionally, this memory was remarkably resistant to acoustic transformations, such as looping and scrambling of the sounds. Finally, implicit recognition of sounds was dependent on participant's discrimination performance during learning. Our findings suggest that meaningless temporal features as short as 10 ms can be implicitly stored in long-term auditory memory. Moreover, successful encoding and storage of such fine features may vary between participants, possibly depending on individual attention and auditory discrimination abilities.

Meaningless auditory patterns could be implicitly encoded and stored in long-term memory.

Acoustic transformations of learned meaningless patterns could be implicitly recognized after 4 weeks.

Implicit long-term memories can be formed for meaningless auditory features as short as 10 ms.